The manufacture of semiconductor integrated circuit chips remains a complex and demanding business, and it continues to rapidly evolve with major changes in device architectures and process technologies. One constant has been testing at each stage of production to ensure that chip quality remains within acceptable limits.
Data from chip testing can be utilized by passing it through a set of engineering rules to decide whether to discard, ship, or further test the chip under consideration. This approach, based primarily on the intuition of test engineers, was adequate in previous device nodes for three reasons. (1) The physical dimensions of the devices were far from the limitations imposed by the materials and the manufacturing process. Thus, the reliability was generally high even if the engineering rules governing the pass/fail decisions were noisy. (2) The tests themselves were simpler in terms of number of the parameters tested. (3) Less complexity in chip design relative to modern day layouts. As a result, test engineers could use their subject-matter expertise to formulate the pass/fail rules with high accuracy.
However, modern chip manufacturing, with process improvements that result in smaller features, tighter tolerances and higher density, increasingly pushes the physical limits of such tests. Reliability concerns as well as improvements in the testing space have resulted in both an increase in number of tests performed as well as an increase in the amount of information obtained per test. Frequently, there are interactions between parameters that are difficult for human experts to uncover by inspection.
Machine Learning (“ML”) algorithms have become popular for use with semiconductor manufacturing processes precisely because they are able to uncover multivariate relationships among data and parameters thereby enhancing the ability to monitor and control the chip production process. Generally, an ML model can be constructed for a specific process parameter by sampling relevant data in order to build one or more training sets of data to represent expected performance of the process with regard to that parameter.
One of the areas of testing that could improve is the determination that a chip in current production is good quality and can proceed, or is bad quality and should be rejected, or is somewhere between good and bad quality and should be subject to further testing. Of course, the determination that a chip is bad or requires further testing has significant cost implications, including the fact that further testing can be fatally harmful to the device.
Therefore, it would be desirable to be effectively filter the chips to exclude those chips that do not require further testing. In this disclosure, an ML model is created using the testing data in order to include or exclude certain chips from further failure testing.